Synthesized View Quality Assessment Using Feature Matching and Superpixel Difference

Mahmoudpour, Saeed; Schelkens, Peter

doi:10.1109/lsp.2020.3024109

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…The proposed metric obtains PLCC and SROCC of 0.7620 and 0.7513, respectively. This is much higher than the PLCC (0.7020) and SROCC (0.6850) of the best performing FR IQA, i.e., SSPD [10]. Additionally, our model also gives better results than the NR IQA, DSCB [9], which has PLCC equal to 0.6030 and SROCC equal to 0.5571.…”

Section: Experiments Results Analysismentioning

confidence: 69%

“…Also, these results will motivate future researchers to use cosine similarity instead of MSE when distortions are not uniformly distributed across the images. Further,in order to show that the proposed algorithm is performing better than the existing ones, we compared the it with the recently proposed five FR (SSPD [10], SC-IQA [14], LOGS [15], MP-PSNR [16], MW-PSNR [17]) and seven NR quality assessment algorithms (Yan's [8], DSCB [9], Wang's [18], APT [3], Jakhetiya's [19], OMIQA [20], NIQSV+ [21]). As depicted in Table 3, the proposed method outperforms the contemporary IQA metrics developed for DIBR synthesized views.…”

Section: Experiments Results Analysismentioning

confidence: 99%

“…Further, Sadbhawna et al [9] suggested a NR QA metric based on the various perceptual characteristics of the 3D views. Mahmoudpour et al put forth a QA metric using the super-pixel and gradient magnitude similarity [10]. Though these metrics work better than the existing metrics yet, they cannot achieve better than the 0.7 value of PLCC.…”

Section: Introductionmentioning

confidence: 99%

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Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features

Chaudhary

Mazumder²,

Mumtaz

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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In recent years, Depth-Image-Based-Rendering (DIBR) synthesized views have gained popularity due to their numerous visual media applications. Consequently, the research in their quality assessment (QA) has also gained momentum. In this work, we propose an efficient metric to estimate the perceptual quality of DIBR synthesized views via the extraction of Deep-features. These Deep-features are extracted from a pretrained CNN model. Generally, in DIBR synthesized views, geometric distortions arise near the objects due to occlusion, and the human visual system is quite sensitive towards these objects. On the other end, saliency maps are efficiently able to highlight perceptually important objects. With this intuition, instead of extracting deep features directly from DIBR synthesized views, we obtain the refined feature vector from their corresponding saliency maps. Also, most of the pixels with geometric distortions have a nearly similar impact on the perceptual quality of 3D synthesized views. Considering this, we propose to fuse the feature maps using the cosine similarity measure based upon the deviation of one feature vector from another. It may also be emphasized that no training is performed in the proposed algorithm, and all the features are extracted from the pre-trained vanilla VGG-16 architecture. The proposed metric, when applied to the standard database, results in PLCC of 0.762 and SRCC equal to 0.7513, which is better than the existing state-of-the-art QA metrics.

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Section: Experiments Results Analysismentioning

confidence: 69%

Section: Experiments Results Analysismentioning

confidence: 99%

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Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features

Chaudhary

Mazumder²,

Mumtaz

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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show abstract

“…SPSIM is not the only IQA model that uses superpixel segmentation. Last year a measure based on local SURF matching and SuperPixel Difference (SSPD) was presented [14]. In this complex model applied to Depth Image-Based Rendering (DIBR) synthesised views, the calculation of gradient magnitude differences at the superpixel level was used as one of three information channels.…”

Section: Related Workmentioning

confidence: 99%

An Improved SPSIM Index for Image Quality Assessment

Frackiewicz¹,

Szolc²,

Palus³

2021

Preprint

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Objective Image Quality Assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by MOS/DMOS scores. One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of the rectangular pixel grid.The authors in this paper have been proposed three modifications of SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from the algorithm for computing the MDSI index. The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained on many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

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“…Although this method is effective for specific types of distortions, it is not sensitive to other types of distortions. Mahmoudpour et al 12 proposed using the feature matching to quantify local differences, which integrates local feature matching and global feature comparison. Ling et al 13 proposed a variable-length context tree-based IQA algorithm, and Battisti et al 14 proposed a 3D synthesized view image quality metric (3D-SwIM).…”

Section: Introductionmentioning

confidence: 99%

No-reference quality assessment for depth image based rendering-synthesized images based on multi-feature fusion

Wang,

Chen,

Lai

et al. 2023

J. Electron. Imag.

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.The depth image based rendering (DIBR) technique plays a crucial role in free viewpoint video. However, the DIBR technology is not yet mature and still needs to be improved. In addition, depth image acquisition is inaccurate and prone to distortion, which affects human visual perception. The DIBR-synthesized image quality assessment (IQA) method, which simulates the human visual perception system, can be used as feedback to improve the DIBR technique and depth video quality. The existing IQA algorithms are insensitive to the diversity of geometric distortion produced by the DIBR technique and do not consider the impact of geometric distortion on the image quality from multiple perspectives. In this work, a no-reference IQA method for DIBR-synthesized images based on multi-feature fusion is proposed. Different from the existing IQA algorithms, this paper focuses on the diversity of geometric distortions in DIBR-synthesized images, measures the distortions of the edge region and the hole region, and integrates the global sharpness and the natural mixed distortion. Specifically, the global sharpness is first measured, and the image is divided into blocks to further improve the accuracy of the results. Secondly, the geometric distortions of the edge and hole regions are measured comprehensively. In addition, considering the impact of the natural mixed distortion on the quality of the DIBR-synthesized image, a Gaussian mixed statistical model is used to extract the natural mixed distortion of the DIBR-synthesized image. Finally, the quality score of the DIBR-synthesized image is generated by pooling regression. Experimental results on two synthesized image databases demonstrate that the method proposed in this paper outperforms classical and state-of-the-art IQA methods and is more aligned with the human visual perception system.

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Synthesized View Quality Assessment Using Feature Matching and Superpixel Difference

Cited by 16 publications

References 32 publications

Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features

Perceptual Quality Assessment of DIBR Synthesized Views Using Saliency Based Deep Features

An Improved SPSIM Index for Image Quality Assessment

No-reference quality assessment for depth image based rendering-synthesized images based on multi-feature fusion

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